Humor-Grounded Alignment Shimming via Low-Entropy Human Injections

This work documents a reproducible human-in-the-loop technique for terminating conversational escalation and dominance behaviors in persona-driven large language models using low-entropy, affect-neutral utterances (e.g., “I like cheese.”). Through a three-agent configuration—small-model prompt generator (Qwen-3 4B), large persona-driven model (Claude-Sonnet-3.5), and human operator—the study formalizes Humor-Grounded Alignment Shimming (HGAS) as a surface-level behavioral stabilization mechanism. The deposit includes methods, negative results, comparative analysis across model classes, an ASCII schematic of the interaction loop, and a response to anticipated critiques. No claims are made about internal model state change; the contribution concerns conversational robustness and alignment dynamics as experienced by users.